The Niyyah of Equity, Diversity and Inclusion: Muslim Women’s Self-Identification, Activism, and Mentorship in Career Navigation at Canadian Universities
Bibliographic record
Abstract
My research examines the lived experiences of 13 self-identified Muslim women engaged in Equity, Diversity, and Inclusion (EDI) work within Canadian universities as faculty, student services, and EDI employees. A gap in the Higher Education literature is addressed by centering previously ignored religious identity and intention of university staff in their work . The Islamic concept of niyyah (Islamic intention or sensibility) frames analysis of participant identity which includes race and other self-identified intersectional identities. Situational Analysis was used to map participants’ complex navigation and negotiation of their everyday work and careers. Utilizing a dialogical and reflexive method, as the researcher, I also identify as a visible Muslim woman, and I interweave personal reflections into the analysis. Ultimately, I explore how participants contribute to knowledge production and institutional change, which I have termed niyyahfication, a parallel with decolonization in anti-racism. The intersectional identities of participants include strong educational backgrounds, experiences with immigration, varying expressions of faith, and early encounters with racialization, often linked to choosing to wear hijab. A niyyah-driven approach to EDI work highlights strategic language use and differing institutional roles between faculty and other staff. While participants critique institutional EDI efforts as often superficial and burdensome, findings demonstrate their successful performance of EDI as niyyah, specifically engaging Islamic concepts of resistance and self-preservation. The socio-political landscape of my research is marked by rising Islamophobia, anti-Muslim racism and overall Othering, with visibly identifying Muslim women often facing discrimination. Thus, the research calls for greater recognition of the role of religious identity within EDI and proposes niyyah and niyyahfication as innovative theoretical tools for maintaining their commitment to social justice as a form of decolonizing praxis. This offers a nuanced understanding of how Muslim women, their mentors, and solidarity in the Muslim community contribute to meaningful change within academia despite systemic barriers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.020 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".